rbf神经网络_学习笔记_matlab doc注解

今天学习了rbf神经网络。
但由于课上讲的内容十分有限，我对于rbf的原理有些模糊，因此在网上找了一些参考资料。
先贴两篇个人觉得有助于理解的博文
https://blog.youkuaiyun.com/weiwei9363/article/details/72808496#commentsedit
https://blog.youkuaiyun.com/ecnu18918079120/article/details/53365341/#commentBox

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笔者阅读了matlab自带的newrbe函数的document，并对其进行了一定的注释：

Here is a radial basis network with R inputs.

Notice that the expression for the net input of a radbas neuron is different from that of other neurons.%%rbf神经网络的输入与其他神经网络不同！
Here the net input to the radbas transfer function is the vector distance between its weight vector w and the input vector p, multiplied by the bias b.
%%传递进neuron的实际上是输入向量与阈值向量之间的距离dist，而这里的阈值向量的选取方法有很多，如kmeans，knn等，最后并对其进行加权(w,b),对函数的sensitivity进行调整，输出n。
The transfer function for a radial basis neuron is

$$radbas(n)=e^{−n^2}$$
%%一般选取高斯函数，这里有点kernel function的味道，作用是将数据点映射至一个高维空间，使其线性可分，之前在seminar的时候有做过详细了解，有时间的话下篇博文再贴。
Here is a plot of the radbas transfer function.

The radial basis function has a maximum of 1 when its input is 0. As the distance between w and p decreases, the output increases. Thus, a radial basis neuron acts as a detector that produces 1 whenever the input p is identical to its weight vector w.

Network Architecture

Radial basis networks consist of two layers: a hidden radial basis layer of S1 neurons, and an output linear laye